Getting started with arules

In arules: Mining Association Rules and Frequent Itemsets

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(arules)
set.seed(1234)

Association rule mining starts with a collection of transactions. Each transaction contains a set of items, such as the products in a shopping basket. This guide introduces the basic workflow: create transactions, inspect the data, mine rules, and select useful results.

Installation

Install the released version of arules from CRAN:

install.packages("arules")

Load the package in each R session where you want to use it:

library(arules)

Create transactions

A named list is the simplest input format for small data sets.

baskets <- list(
  T1 = c("milk", "bread", "butter"),
  T2 = c("bread", "butter"),
  T3 = c("milk", "bread"),
  T4 = c("bread", "jam"),
  T5 = c("milk", "bread", "butter"),
  T6 = c("beer", "chips"),
  T7 = c("beer", "chips", "salsa"),
  T8 = c("bread", "butter", "jam")
)
trans <- transactions(baskets)
trans
inspect(trans[1:3])

summary() describes the sparse transaction matrix. itemFrequency() returns the fraction of transactions containing each item.

summary(trans)
sort(itemFrequency(trans), decreasing = TRUE)

Mine and inspect rules

apriori() mines association rules. Support specifies how often all items in a rule must occur together, confidence specifies how often the right-hand side must occur when the left-hand side occurs, and maxlen limits the total number of items in a rule.

On large data sets, setting support too low or maxlen too high can produce an extremely large rule set and exhaust the available memory. Start with restrictive values and relax them only as needed.

rules <- apriori(
  trans,
  parameter = list(support = 0.25, confidence = 0.6, maxlen = 5),
  control = list(verbose = FALSE)
)
rules

Rules are often sorted by an interest measure before inspection. Lift is a common choice.

inspect(sort(rules, by = "lift"))

Use ordinary subsetting expressions to focus on a particular consequent or a minimum quality value.

butter_rules <- subset(rules, rhs %in% "butter" & lift > 1)
inspect(butter_rules)

Other vignettes

• Preparing transaction data
• Mining and pruning association rules
• Interest measures
• Item hierarchies

To explore association rules visually, see the arulesViz package.

arules documentation built on Sept. 11, 2026, 9:08 a.m.